{"doi":"10.1167/tvst.12.3.9","title":"AxoNet 2.0: A Deep Learning-Based Tool for Morphometric Analysis of Retinal Ganglion Cell Axons","abstract":"Purpose: Assessment of glaucomatous damage in animal models is facilitated by rapid and accurate quantification of retinal ganglion cell (RGC) axonal loss and morphologic change. However, manual assessment is extremely time- and labor-intensive. Here, we developed AxoNet 2.0, an automated deep learning (DL) tool that (i) counts normal-appearing RGC axons and (ii) quantifies their morphometry from light micrographs. Methods: A DL algorithm was trained to segment the axoplasm and myelin sheath of normal-appearing axons using manually-annotated rat optic nerve (ON) cross-sectional micrographs. Performance was quantified by various metrics (e.g., soft-Dice coefficient between predicted and ground-truth segmentations). We also quantified axon counts, axon density, and axon size distributions between hypertensive and control eyes and compared to literature reports. Results: AxoNet 2.0 performed very well when compared to manual annotations of rat ON (R2 = 0.92 for automated vs. manual counts, soft-Dice coefficient = 0.81 ± 0.02, mean absolute percentage error in axonal morphometric outcomes < 15%). AxoNet 2.0 also showed promise for generalization, performing well on other animal models (R2 = 0.97 between automated versus manual counts for mice and 0.98 for non-human primates). As expected, the algorithm detected decreased in axon density in hypertensive rat eyes (P ≪ 0.001) with preferential loss of large axons (P < 0.001). Conclusions: AxoNet 2.0 provides a fast and nonsubjective tool to quantify both RGC axon counts and morphological features, thus assisting with assessing axonal damage in animal models of glaucomatous optic neuropathy. Translational Relevance: This deep learning approach will increase rigor of basic science studies designed to investigate RGC axon protection and regeneration.","journal":"Translational Vision Science & Technology","year":2023,"id":329847,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":17,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9552,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":336414,"name":"A. Thomas Read","orcid":"0000-0002-2764-6413","position":1,"is_corresponding":false},{"id":338174,"name":"Matthew D. Ritch","orcid":null,"position":2,"is_corresponding":false},{"id":336413,"name":"Bailey G. Hannon","orcid":"0000-0001-9141-8848","position":3,"is_corresponding":false},{"id":1053518,"name":"Gabriela Sanchez Rodriguez","orcid":null,"position":4,"is_corresponding":false},{"id":574595,"name":"Dillon M. Brown","orcid":"0000-0003-0791-0309","position":5,"is_corresponding":false},{"id":336415,"name":"Andrew Feola","orcid":"0000-0002-7914-989X","position":6,"is_corresponding":false},{"id":339412,"name":"Adam Hedberg‐Buenz","orcid":"0000-0003-0999-4825","position":7,"is_corresponding":false},{"id":338175,"name":"Grant Cull","orcid":null,"position":8,"is_corresponding":false},{"id":336416,"name":"Juan Reynaud","orcid":"0000-0003-2624-9229","position":9,"is_corresponding":false},{"id":820564,"name":"Mona K. Garvin","orcid":"0000-0003-3299-3150","position":10,"is_corresponding":false},{"id":339413,"name":"Michael G. Anderson","orcid":"0000-0001-5730-6105","position":11,"is_corresponding":false},{"id":336417,"name":"Claude F. Burgoyne","orcid":"0000-0002-2765-4739","position":12,"is_corresponding":false},{"id":336418,"name":"C. Ross Ethier","orcid":"0000-0001-6110-3052","position":13,"is_corresponding":false},{"id":715572,"name":"Vidisha Goyal","orcid":"0000-0001-8065-5522","position":0,"is_corresponding":true}],"reference_count":49,"raw_metadata":null,"created_at":"2026-07-19T01:09:05.843139Z","pmid":"36917117","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}